Group 2

Introduction

Data sourced from paper:

“Apoptosis and other immune biomarkers predict influenza vaccine responsiveness”

Focus of current project:

Clean and augment data for analysis
PCA
Age analysis on antibody response to vaccine
Prediction of vaccine response based on Probes signal

Flow chart

Made with draw.io

Data description

numeric_summary <- analysis_data |>
  group_by(Age_Group) |>
  summarize(Age_Range = str_c(
      round(median(Age, na.rm = TRUE), 1), 
      " (", 
      min(Age, na.rm = TRUE), 
      "–", 
      max(Age, na.rm = TRUE), 
      ")"),
    BMI_Range = str_c(
      round(median(BMI, na.rm = TRUE), 1), 
      " (", 
      min(BMI, na.rm = TRUE), 
      "–", 
      max(BMI, na.rm = TRUE), 
      ")"),
    .groups = 'drop')

# A tibble: 2 × 3
  Age_Group Age_Range  BMI_Range       
  <chr>     <chr>      <chr>           
1 Older     78 (61–93) 25.1 (18–47.3)  
2 Young     24 (20–30) 22.9 (18.8–43.6)

gender_table <- get_categorical_summary(analysis_data, Gender) |> 
  mutate(Variable = "Gender")
cmv_table <- get_categorical_summary(analysis_data, Cytomegalovirus) |> 
  mutate(Variable = "Cytomegalovirus")
ebv_table <- get_categorical_summary(analysis_data, EpsteinBarrvirus) |> 
  mutate(Variable = "EpsteinBarrvirus")

categorical_summary <- bind_rows(gender_table, cmv_table, ebv_table)

final_categorical_table <- categorical_summary |>
  pivot_wider(names_from = Age_Group,
              values_from = Value,
              id_cols = c(Variable, Characteristic),
              names_sort = TRUE)

# A tibble: 6 × 4
  Variable         Characteristic Older    Young   
  <chr>            <chr>          <chr>    <chr>   
1 Gender           Female         40 (67%) 14 (48%)
2 Gender           Male           20 (33%) 15 (52%)
3 Cytomegalovirus  Negative       24 (40%) 13 (45%)
4 Cytomegalovirus  Positive       36 (60%) 16 (55%)
5 EpsteinBarrvirus Negative       19 (32%) 13 (45%)
6 EpsteinBarrvirus Positive       41 (68%) 16 (55%)

Plots from paper

PCA

Not well separated but PC2 and PC3 most separated

Biomarkers for prediction of response

Based on the plot, we observe that the most differentiated probes are ILMN_1688780 and ILMN_1739792

To find the most significant Probe that could potentially predict vaccine response we create a Boxplot for the two Probes observed previously.

ILMN_1688780 has the most clear and non-overlapping difference in the distribution of pre-vaccine expression between the two groups.